CoreModel.pa_ex(*, scenario: str = 'baseline')

Return activity-side exploded price-index contributions for SUT.

\[\begin{split}p_{a,\mathrm{ex}} = \begin{bmatrix} \operatorname{diag}(v_a^{\mathsf{T}}\mathbf{1})w_{aa} \\ \operatorname{diag}(v_c^{\mathsf{T}}\mathbf{1})w_{ca} \end{bmatrix}\end{split}\]

Here each value-added coefficient vector is summed over factor rows before diagonalization. The two blocks are stacked on rows: activity contributors first, then commodity contributors. Column sums recover pa. Here \(w_{ca}=u\,w_{aa}\).

This is the aggregated analogue of ma_ex(): it sums factor rows first and returns activity and commodity contributors stacked on the rows.

Parameters:

scenario – Scenario used to read source matrices.

Returns:

DataFrame whose rows describe the contributing activity or commodity and whose column sums equal pa.

Return type:

pandas.DataFrame

Notes

For IOT models use p_ex(). For the commodity side use pc_ex().

The full baseline result is also available as the property db.pa_ex_all.